To evaluate the performance of our color segmentation system,
we collected a set of 21 video sequences from nine popular DVD movies. Collected
sequences vary in length from 50 to 350 frames; most, however, are in the
70 to 100 frame range. All experimental sequences were hand-labeled to provide
the ground truth data for algorithm performance verification. This data was
used in evaluating the system described in: Sigal, L., and Sclaroff, S.,
Estimation and Prediction of Evolving Color Distributions for Skin Segmentation
Under Varying IlluminationProc. IEEE Conf. on Computer Vision and
Pattern Recognition, (CVPR), June, 2000.

This dataset contains 107,328 images of a realistic computer graphics rendering
of realistic human hand model. Ground truth for each image is available, thus
enabling quantitative evaluation of articulated pose esimtation algorithms.
More than 200 real images of hands are also distributed with this dataset.
The dataset was used in evaluating the system described in: Vassilis Athitsos
and Stan Sclaroff,
Estimating 3D Hand Pose From a Cluttered Image, Proc. IEEE Conf. on Computer
Vision and Pattern Recognition (CVPR), Vol. 2, pp 432-439, 2003.